A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concept...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2016-06, Vol.11 (6), p.e0157428-e0157428
Main Authors: Ali, Nouman, Bajwa, Khalid Bashir, Sablatnig, Robert, Chatzichristofis, Savvas A, Iqbal, Zeshan, Rashid, Muhammad, Habib, Hafiz Adnan
Format: Article
Language:English
Subjects:
Algorithms
Archives
Artificial Intelligence
Automation
Benchmarks
Biology and Life Sciences
Color
Computer and Information Sciences
Computer engineering
Earth Sciences
Fourier transforms
Ground truth
Image Interpretation, Computer-Assisted
Image management
Image Processing, Computer-Assisted
Image retrieval
Information Storage and Retrieval
Integration
International conferences
Levels
Multimedia
Noise
Pattern Recognition, Automated
Queries
Research and Analysis Methods
Retrieval
Robustness
Semantics
Support Vector Machine
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0157428